Overview

Dataset statistics

Number of variables18
Number of observations1000
Missing cells41
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory152.5 KiB
Average record size in memory156.1 B

Variable types

Categorical7
Numeric9
DateTime2

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072890/fileData.do

Alerts

UPDT_BRCD is highly overall correlated with RMT_BNK_CD and 5 other fieldsHigh correlation
ACCT_DEPT_CD is highly overall correlated with RMT_BNK_CD and 5 other fieldsHigh correlation
DECIS_NO is highly overall correlated with ACCT_DVCDHigh correlation
DEPT_CD is highly overall correlated with RMT_BNK_CD and 5 other fieldsHigh correlation
REG_BRCD is highly overall correlated with RMT_BNK_CD and 5 other fieldsHigh correlation
ACCT_DVCD is highly overall correlated with DECIS_DY and 14 other fieldsHigh correlation
STLE_CD is highly overall correlated with DEPT_CD and 4 other fieldsHigh correlation
DECIS_DY is highly overall correlated with ACCT_DY and 3 other fieldsHigh correlation
CNFM_ENO is highly overall correlated with ACCT_DVCDHigh correlation
RMT_BNK_CD is highly overall correlated with DEPT_CD and 4 other fieldsHigh correlation
ACCT_DY is highly overall correlated with DECIS_DY and 3 other fieldsHigh correlation
SLIP_NO is highly overall correlated with ACCT_DVCDHigh correlation
UPDT_ENO is highly overall correlated with REG_ENO and 1 other fieldsHigh correlation
REG_ENO is highly overall correlated with UPDT_ENO and 1 other fieldsHigh correlation
GENRT_DY is highly overall correlated with DECIS_DY and 3 other fieldsHigh correlation
CARD_STTL_DY is highly overall correlated with DECIS_DY and 3 other fieldsHigh correlation
DECIS_NO is highly imbalanced (85.4%)Imbalance
STLE_CD is highly imbalanced (82.0%)Imbalance
ACCT_DVCD is highly imbalanced (92.6%)Imbalance

Reproduction

Analysis started2023-12-12 17:37:18.065388
Analysis finished2023-12-12 17:37:30.579840
Duration12.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DEPT_CD
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
ACS
88 
TLB
 
48
TAA
 
48
TOA
 
48
TAB
 
48
Other values (21)
720 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHA
2nd rowTNA
3rd rowTAC
4th rowTBB
5th rowTAD

Common Values

ValueCountFrequency (%)
ACS 88
 
8.8%
TLB 48
 
4.8%
TAA 48
 
4.8%
TOA 48
 
4.8%
TAB 48
 
4.8%
TPA 47
 
4.7%
THO 46
 
4.6%
QAD 45
 
4.5%
TAC 45
 
4.5%
TPB 44
 
4.4%
Other values (16) 493
49.3%

Length

2023-12-13T02:37:30.669863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acs 88
 
8.8%
taa 48
 
4.8%
toa 48
 
4.8%
tab 48
 
4.8%
tlb 48
 
4.8%
tpa 47
 
4.7%
tho 46
 
4.6%
qad 45
 
4.5%
tac 45
 
4.5%
tad 44
 
4.4%
Other values (16) 493
49.3%

DECIS_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct302
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20185783
Minimum20170223
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:30.830496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170223
5-th percentile20170512
Q120180221
median20190123
Q320191125
95-th percentile20200820
Maximum20201026
Range30803
Interquartile range (IQR)10904.25

Descriptive statistics

Standard deviation10687.372
Coefficient of variation (CV)0.00052945046
Kurtosis-1.257933
Mean20185783
Median Absolute Deviation (MAD)9801
Skewness-0.036945547
Sum2.0185783 × 1010
Variance1.1421993 × 108
MonotonicityNot monotonic
2023-12-13T02:37:31.004517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170424 11
 
1.1%
20170724 11
 
1.1%
20180724 10
 
1.0%
20191021 10
 
1.0%
20190325 10
 
1.0%
20180524 10
 
1.0%
20171023 9
 
0.9%
20170925 9
 
0.9%
20190924 9
 
0.9%
20200219 9
 
0.9%
Other values (292) 902
90.2%
ValueCountFrequency (%)
20170223 1
 
0.1%
20170224 5
0.5%
20170315 1
 
0.1%
20170317 3
0.3%
20170320 3
0.3%
20170321 1
 
0.1%
20170322 1
 
0.1%
20170323 5
0.5%
20170324 7
0.7%
20170412 1
 
0.1%
ValueCountFrequency (%)
20201026 2
0.2%
20201022 2
0.2%
20201021 2
0.2%
20201020 1
 
0.1%
20201019 2
0.2%
20201016 1
 
0.1%
20201015 3
0.3%
20201013 1
 
0.1%
20201006 1
 
0.1%
20200928 1
 
0.1%

DECIS_NO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
948 
2
 
43
3
 
7
5
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 948
94.8%
2 43
 
4.3%
3 7
 
0.7%
5 1
 
0.1%
4 1
 
0.1%

Length

2023-12-13T02:37:31.149858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:31.253828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 948
94.8%
2 43
 
4.3%
3 7
 
0.7%
5 1
 
0.1%
4 1
 
0.1%

STLE_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
957 
2
 
35
<NA>
 
8

Length

Max length4
Median length1
Mean length1.024
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
1 957
95.7%
2 35
 
3.5%
<NA> 8
 
0.8%

Length

2023-12-13T02:37:31.387535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:31.504265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 957
95.7%
2 35
 
3.5%
na 8
 
0.8%

CNFM_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)8.1%
Missing7
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1243.7422
Minimum1009
Maximum6078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:31.639002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile1095
Q11153
median1201
Q31295
95-th percentile1377
Maximum6078
Range5069
Interquartile range (IQR)142

Descriptive statistics

Standard deviation319.86771
Coefficient of variation (CV)0.25718168
Kurtosis205.94829
Mean1243.7422
Median Absolute Deviation (MAD)59
Skewness13.808715
Sum1235036
Variance102315.35
MonotonicityNot monotonic
2023-12-13T02:37:31.817814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1212 62
 
6.2%
1377 39
 
3.9%
1127 38
 
3.8%
1187 38
 
3.8%
1198 34
 
3.4%
1142 34
 
3.4%
1360 33
 
3.3%
1183 33
 
3.3%
1235 29
 
2.9%
1205 26
 
2.6%
Other values (70) 627
62.7%
ValueCountFrequency (%)
1009 2
 
0.2%
1037 15
1.5%
1086 3
 
0.3%
1087 11
1.1%
1095 23
2.3%
1103 1
 
0.1%
1117 5
 
0.5%
1118 10
1.0%
1121 13
1.3%
1122 7
 
0.7%
ValueCountFrequency (%)
6078 3
 
0.3%
6015 1
 
0.1%
1499 1
 
0.1%
1421 2
 
0.2%
1419 16
1.6%
1395 7
 
0.7%
1394 7
 
0.7%
1393 4
 
0.4%
1377 39
3.9%
1375 7
 
0.7%

RMT_BNK_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.282
Minimum2
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:31.987789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q332
95-th percentile88
Maximum88
Range86
Interquartile range (IQR)28

Descriptive statistics

Standard deviation33.253846
Coefficient of variation (CV)1.4283071
Kurtosis-0.37680394
Mean23.282
Median Absolute Deviation (MAD)1
Skewness1.2346031
Sum23282
Variance1105.8183
MonotonicityNot monotonic
2023-12-13T02:37:32.389126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 455
45.5%
3 232
23.2%
81 130
 
13.0%
88 94
 
9.4%
11 46
 
4.6%
32 26
 
2.6%
39 16
 
1.6%
2 1
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 232
23.2%
4 455
45.5%
11 46
 
4.6%
32 26
 
2.6%
39 16
 
1.6%
81 130
 
13.0%
88 94
 
9.4%
ValueCountFrequency (%)
88 94
 
9.4%
81 130
 
13.0%
39 16
 
1.6%
32 26
 
2.6%
11 46
 
4.6%
4 455
45.5%
3 232
23.2%
2 1
 
0.1%

ACCT_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
991 
<NA>
 
9

Length

Max length4
Median length1
Mean length1.027
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row<NA>
4th row2
5th row<NA>

Common Values

ValueCountFrequency (%)
2 991
99.1%
<NA> 9
 
0.9%

Length

2023-12-13T02:37:32.532818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:32.657520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 991
99.1%
na 9
 
0.9%

ACCT_DEPT_CD
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
ACS
88 
TLB
 
48
TOA
 
48
TAB
 
48
TAA
 
47
Other values (22)
721 

Length

Max length4
Median length3
Mean length3.009
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHA
2nd rowTNA
3rd row<NA>
4th rowTBB
5th row<NA>

Common Values

ValueCountFrequency (%)
ACS 88
 
8.8%
TLB 48
 
4.8%
TOA 48
 
4.8%
TAB 48
 
4.8%
TAA 47
 
4.7%
TPA 46
 
4.6%
THO 45
 
4.5%
THA 44
 
4.4%
TAC 44
 
4.4%
QAD 44
 
4.4%
Other values (17) 498
49.8%

Length

2023-12-13T02:37:32.800502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acs 88
 
8.8%
toa 48
 
4.8%
tab 48
 
4.8%
tlb 48
 
4.8%
taa 47
 
4.7%
tpa 46
 
4.6%
tho 45
 
4.5%
tha 44
 
4.4%
tac 44
 
4.4%
qad 44
 
4.4%
Other values (17) 498
49.8%

ACCT_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)10.0%
Missing9
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean20185686
Minimum20170223
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:32.949285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170223
5-th percentile20170474
Q120180174
median20190124
Q320191125
95-th percentile20200825
Maximum20201026
Range30803
Interquartile range (IQR)10951.5

Descriptive statistics

Standard deviation10664.178
Coefficient of variation (CV)0.00052830396
Kurtosis-1.2548762
Mean20185686
Median Absolute Deviation (MAD)9798
Skewness-0.027847584
Sum2.0004015 × 1010
Variance1.1372469 × 108
MonotonicityNot monotonic
2023-12-13T02:37:33.109070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191125 26
 
2.6%
20200525 26
 
2.6%
20200825 24
 
2.4%
20170724 24
 
2.4%
20190524 23
 
2.3%
20190225 23
 
2.3%
20181224 23
 
2.3%
20200625 23
 
2.3%
20200427 23
 
2.3%
20170925 23
 
2.3%
Other values (89) 753
75.3%
ValueCountFrequency (%)
20170223 1
 
0.1%
20170224 5
 
0.5%
20170323 2
 
0.2%
20170324 19
1.9%
20170412 1
 
0.1%
20170424 21
2.1%
20170425 1
 
0.1%
20170523 3
 
0.3%
20170524 18
1.8%
20170623 2
 
0.2%
ValueCountFrequency (%)
20201026 7
 
0.7%
20200928 1
 
0.1%
20200925 22
2.2%
20200825 24
2.4%
20200730 1
 
0.1%
20200729 1
 
0.1%
20200727 21
2.1%
20200625 23
2.3%
20200525 26
2.6%
20200427 23
2.3%

SLIP_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)21.0%
Missing9
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean77.810293
Minimum1
Maximum2989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:33.261696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median10
Q364
95-th percentile241
Maximum2989
Range2988
Interquartile range (IQR)63

Descriptive statistics

Standard deviation243.36999
Coefficient of variation (CV)3.1277352
Kurtosis52.323156
Mean77.810293
Median Absolute Deviation (MAD)9
Skewness6.614741
Sum77110
Variance59228.954
MonotonicityNot monotonic
2023-12-13T02:37:33.395995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 361
36.1%
4 31
 
3.1%
2 28
 
2.8%
7 21
 
2.1%
3 20
 
2.0%
13 15
 
1.5%
5 12
 
1.2%
16 10
 
1.0%
6 10
 
1.0%
28 9
 
0.9%
Other values (198) 474
47.4%
ValueCountFrequency (%)
1 361
36.1%
2 28
 
2.8%
3 20
 
2.0%
4 31
 
3.1%
5 12
 
1.2%
6 10
 
1.0%
7 21
 
2.1%
8 5
 
0.5%
9 2
 
0.2%
10 7
 
0.7%
ValueCountFrequency (%)
2989 1
0.1%
2322 1
0.1%
2162 1
0.1%
2153 1
0.1%
2118 1
0.1%
1617 1
0.1%
1616 1
0.1%
1465 1
0.1%
1407 1
0.1%
1384 1
0.1%
Distinct904
Distinct (%)91.1%
Missing8
Missing (%)0.8%
Memory size7.9 KiB
Minimum2017-02-23 14:50:00
Maximum2020-10-26 09:43:00
2023-12-13T02:37:33.562297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:33.721965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

UPDT_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)9.1%
Missing8
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5980.3206
Minimum1118
Maximum52549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:33.862603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1118
5-th percentile1818
Q16033
median6054
Q36076
95-th percentile7626
Maximum52549
Range51431
Interquartile range (IQR)43

Descriptive statistics

Standard deviation2856.1045
Coefficient of variation (CV)0.47758385
Kurtosis212.25781
Mean5980.3206
Median Absolute Deviation (MAD)22
Skewness12.884671
Sum5932478
Variance8157333.1
MonotonicityNot monotonic
2023-12-13T02:37:34.003297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6050 75
 
7.5%
6054 47
 
4.7%
6076 45
 
4.5%
6040 44
 
4.4%
6066 44
 
4.4%
6002 42
 
4.2%
6063 37
 
3.7%
6077 36
 
3.6%
6012 34
 
3.4%
6033 30
 
3.0%
Other values (80) 558
55.8%
ValueCountFrequency (%)
1118 1
 
0.1%
1214 4
0.4%
1256 7
0.7%
1585 1
 
0.1%
1592 1
 
0.1%
1596 2
 
0.2%
1603 7
0.7%
1618 3
0.3%
1641 2
 
0.2%
1648 2
 
0.2%
ValueCountFrequency (%)
52549 2
 
0.2%
52485 1
 
0.1%
7763 1
 
0.1%
7759 2
 
0.2%
7711 10
1.0%
7706 7
0.7%
7682 5
 
0.5%
7656 13
1.3%
7626 14
1.4%
7610 3
 
0.3%

UPDT_BRCD
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
ACS
88 
TLB
 
48
TOA
 
48
TAB
 
48
TAA
 
47
Other values (22)
721 

Length

Max length4
Median length3
Mean length3.008
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHA
2nd rowTNA
3rd row<NA>
4th rowTBB
5th row<NA>

Common Values

ValueCountFrequency (%)
ACS 88
 
8.8%
TLB 48
 
4.8%
TOA 48
 
4.8%
TAB 48
 
4.8%
TAA 47
 
4.7%
TPA 46
 
4.6%
THO 46
 
4.6%
THA 44
 
4.4%
TAC 44
 
4.4%
QAD 44
 
4.4%
Other values (17) 497
49.7%

Length

2023-12-13T02:37:34.135606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acs 88
 
8.8%
toa 48
 
4.8%
tab 48
 
4.8%
tlb 48
 
4.8%
taa 47
 
4.7%
tpa 46
 
4.6%
tho 46
 
4.6%
tha 44
 
4.4%
tac 44
 
4.4%
qad 44
 
4.4%
Other values (17) 497
49.7%

REG_TS
Date

Distinct979
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2017-02-23 14:39:00
Maximum2020-10-26 09:43:00
2023-12-13T02:37:34.265838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:34.407087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5858.766
Minimum1214
Maximum7763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:34.535788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1214
5-th percentile1815
Q16033
median6054
Q36076
95-th percentile7626
Maximum7763
Range6549
Interquartile range (IQR)43

Descriptive statistics

Standard deviation1224.4367
Coefficient of variation (CV)0.20899224
Kurtosis7.1512426
Mean5858.766
Median Absolute Deviation (MAD)22
Skewness-2.6306843
Sum5858766
Variance1499245.1
MonotonicityNot monotonic
2023-12-13T02:37:34.664867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6050 77
 
7.7%
6066 47
 
4.7%
6076 46
 
4.6%
6040 46
 
4.6%
6054 44
 
4.4%
6002 43
 
4.3%
6077 38
 
3.8%
6063 38
 
3.8%
6012 34
 
3.4%
6033 32
 
3.2%
Other values (61) 555
55.5%
ValueCountFrequency (%)
1214 4
0.4%
1256 7
0.7%
1596 2
 
0.2%
1603 8
0.8%
1618 3
 
0.3%
1648 1
 
0.1%
1681 1
 
0.1%
1688 4
0.4%
1692 2
 
0.2%
1694 1
 
0.1%
ValueCountFrequency (%)
7763 1
 
0.1%
7759 1
 
0.1%
7711 10
1.0%
7706 7
0.7%
7682 5
 
0.5%
7656 15
1.5%
7626 14
1.4%
7610 2
 
0.2%
7579 13
1.3%
6202 5
 
0.5%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
ACS
88 
TLB
 
48
TAA
 
48
TOA
 
48
TAB
 
48
Other values (21)
720 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTHA
2nd rowTNA
3rd rowTAC
4th rowTBB
5th rowTAD

Common Values

ValueCountFrequency (%)
ACS 88
 
8.8%
TLB 48
 
4.8%
TAA 48
 
4.8%
TOA 48
 
4.8%
TAB 48
 
4.8%
TPA 47
 
4.7%
THO 46
 
4.6%
QAD 45
 
4.5%
TAC 45
 
4.5%
TPB 44
 
4.4%
Other values (16) 493
49.3%

Length

2023-12-13T02:37:34.788292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
acs 88
 
8.8%
taa 48
 
4.8%
toa 48
 
4.8%
tab 48
 
4.8%
tlb 48
 
4.8%
tpa 47
 
4.7%
tho 46
 
4.6%
qad 45
 
4.5%
tac 45
 
4.5%
tad 44
 
4.4%
Other values (16) 493
49.3%

GENRT_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct302
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20185783
Minimum20170223
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:34.917007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170223
5-th percentile20170512
Q120180221
median20190123
Q320191125
95-th percentile20200820
Maximum20201026
Range30803
Interquartile range (IQR)10904.25

Descriptive statistics

Standard deviation10687.372
Coefficient of variation (CV)0.00052945046
Kurtosis-1.257933
Mean20185783
Median Absolute Deviation (MAD)9801
Skewness-0.036945547
Sum2.0185783 × 1010
Variance1.1421993 × 108
MonotonicityNot monotonic
2023-12-13T02:37:35.054526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170424 11
 
1.1%
20170724 11
 
1.1%
20180724 10
 
1.0%
20191021 10
 
1.0%
20190325 10
 
1.0%
20180524 10
 
1.0%
20171023 9
 
0.9%
20170925 9
 
0.9%
20190924 9
 
0.9%
20200219 9
 
0.9%
Other values (292) 902
90.2%
ValueCountFrequency (%)
20170223 1
 
0.1%
20170224 5
0.5%
20170315 1
 
0.1%
20170317 3
0.3%
20170320 3
0.3%
20170321 1
 
0.1%
20170322 1
 
0.1%
20170323 5
0.5%
20170324 7
0.7%
20170412 1
 
0.1%
ValueCountFrequency (%)
20201026 2
0.2%
20201022 2
0.2%
20201021 2
0.2%
20201020 1
 
0.1%
20201019 2
0.2%
20201016 1
 
0.1%
20201015 3
0.3%
20201013 1
 
0.1%
20201006 1
 
0.1%
20200928 1
 
0.1%

CARD_STTL_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20185813
Minimum20170223
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T02:37:35.214626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170223
5-th percentile20170518
Q120180225
median20190124
Q320191125
95-th percentile20200825
Maximum20201026
Range30803
Interquartile range (IQR)10899.75

Descriptive statistics

Standard deviation10704.795
Coefficient of variation (CV)0.0005303128
Kurtosis-1.2618778
Mean20185813
Median Absolute Deviation (MAD)9798
Skewness-0.039420524
Sum2.0185813 × 1010
Variance1.1459264 × 108
MonotonicityNot monotonic
2023-12-13T02:37:35.367117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191125 26
 
2.6%
20200525 26
 
2.6%
20200825 24
 
2.4%
20170724 24
 
2.4%
20190225 23
 
2.3%
20170925 23
 
2.3%
20190924 23
 
2.3%
20190624 23
 
2.3%
20181224 23
 
2.3%
20190524 23
 
2.3%
Other values (89) 762
76.2%
ValueCountFrequency (%)
20170223 1
 
0.1%
20170224 5
 
0.5%
20170323 2
 
0.2%
20170324 19
1.9%
20170412 1
 
0.1%
20170424 21
2.1%
20170425 1
 
0.1%
20170523 3
 
0.3%
20170524 18
1.8%
20170623 2
 
0.2%
ValueCountFrequency (%)
20201026 15
1.5%
20200928 1
 
0.1%
20200925 22
2.2%
20200825 24
2.4%
20200730 1
 
0.1%
20200729 1
 
0.1%
20200727 21
2.1%
20200625 23
2.3%
20200525 26
2.6%
20200427 23
2.3%

Interactions

2023-12-13T02:37:28.484480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:19.417361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.545079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.592726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.514988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.609960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.745671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.207223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.420123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.635985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:19.526150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.662133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.693297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.616684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.725308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.887488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.351406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.570724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.798883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:19.652831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.784071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.789875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.723336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.843859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.025919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.484682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.697147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.921299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:19.781754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.888530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.880557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.839864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.950332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.411206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.587438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.791053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.066326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:19.903765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.993426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.981383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.987739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.081032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.524091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.698361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.903053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.254729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.029075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.093473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.101134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.115486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.193199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.633313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.818260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.017010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.418404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.192340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.235620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.205452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.243439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.354710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.768065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.925677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.136189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.584904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.302270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.352324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.303085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.365545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.492496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:25.943172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.180492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.248602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.717154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:20.425541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:21.488563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:22.397197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:23.501732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:24.622841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:26.090399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:27.320218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.363916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:37:35.471084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DEPT_CDDECIS_DYDECIS_NOSTLE_CDCNFM_ENORMT_BNK_CDACCT_DEPT_CDACCT_DYSLIP_NOUPDT_ENOUPDT_BRCDREG_ENOREG_BRCDGENRT_DYCARD_STTL_DY
DEPT_CD1.0000.0870.3460.6350.1970.9981.0000.0960.4110.5851.0000.6361.0000.0870.085
DECIS_DY0.0871.0000.0470.2630.0640.1010.0991.0000.1910.0730.0990.2490.0871.0001.000
DECIS_NO0.3460.0471.0000.0000.0000.1030.3450.0470.3970.0430.3450.0320.3460.0470.045
STLE_CD0.6350.2630.0001.0000.0000.3480.6340.2630.0000.0170.6350.0690.6350.2630.263
CNFM_ENO0.1970.0640.0000.0001.0000.0000.1960.0640.0000.0000.1970.0000.1970.0640.065
RMT_BNK_CD0.9980.1010.1030.3480.0001.0000.9980.1040.0000.1610.9980.1830.9980.1010.103
ACCT_DEPT_CD1.0000.0990.3450.6340.1960.9981.0000.0960.4110.5861.0000.6371.0000.0990.096
ACCT_DY0.0961.0000.0470.2630.0640.1040.0961.0000.1900.0740.0960.2450.0961.0001.000
SLIP_NO0.4110.1910.3970.0000.0000.0000.4110.1901.0000.0000.4110.1270.4110.1910.190
UPDT_ENO0.5850.0730.0430.0170.0000.1610.5860.0740.0001.0000.5850.6570.5850.0730.074
UPDT_BRCD1.0000.0990.3450.6350.1970.9981.0000.0960.4110.5851.0000.6381.0000.0990.096
REG_ENO0.6360.2490.0320.0690.0000.1830.6370.2450.1270.6570.6381.0000.6360.2490.250
REG_BRCD1.0000.0870.3460.6350.1970.9981.0000.0960.4110.5851.0000.6361.0000.0870.085
GENRT_DY0.0871.0000.0470.2630.0640.1010.0991.0000.1910.0730.0990.2490.0871.0001.000
CARD_STTL_DY0.0851.0000.0450.2630.0650.1030.0961.0000.1900.0740.0960.2500.0851.0001.000
2023-12-13T02:37:35.625798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
UPDT_BRCDACCT_DEPT_CDDECIS_NODEPT_CDREG_BRCDACCT_DVCDSTLE_CD
UPDT_BRCD1.0001.0000.1711.0001.0001.0000.504
ACCT_DEPT_CD1.0001.0000.1711.0001.0001.0000.504
DECIS_NO0.1710.1711.0000.1720.1721.0000.000
DEPT_CD1.0001.0000.1721.0001.0001.0000.504
REG_BRCD1.0001.0000.1721.0001.0001.0000.504
ACCT_DVCD1.0001.0001.0001.0001.0001.0001.000
STLE_CD0.5040.5040.0000.5040.5041.0001.000
2023-12-13T02:37:35.746465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DECIS_DYCNFM_ENORMT_BNK_CDACCT_DYSLIP_NOUPDT_ENOREG_ENOGENRT_DYCARD_STTL_DYDEPT_CDDECIS_NOSTLE_CDACCT_DVCDACCT_DEPT_CDUPDT_BRCDREG_BRCD
DECIS_DY1.0000.3150.0561.000-0.0380.1660.2031.0001.0000.0460.0390.1741.0000.0520.0520.046
CNFM_ENO0.3151.0000.0690.315-0.0130.1310.1350.3150.3170.1550.0000.0001.0000.1540.1540.155
RMT_BNK_CD0.0560.0691.0000.060-0.214-0.080-0.0670.0560.0610.9850.0390.4241.0000.9850.9850.985
ACCT_DY1.0000.3150.0601.000-0.0380.1630.2071.0001.0000.0500.0380.1741.0000.0500.0500.050
SLIP_NO-0.038-0.013-0.214-0.0381.000-0.028-0.042-0.038-0.0380.1750.2570.0001.0000.1750.1750.175
UPDT_ENO0.1660.131-0.0800.163-0.0281.0000.9110.1660.1620.3650.0320.0281.0000.3650.3650.365
REG_ENO0.2030.135-0.0670.207-0.0420.9111.0000.2030.1990.3830.0180.0411.0000.3830.3840.383
GENRT_DY1.0000.3150.0561.000-0.0380.1660.2031.0001.0000.0460.0390.1741.0000.0520.0520.046
CARD_STTL_DY1.0000.3170.0611.000-0.0380.1620.1991.0001.0000.0450.0370.1741.0000.0500.0510.045
DEPT_CD0.0460.1550.9850.0500.1750.3650.3830.0460.0451.0000.1720.5041.0001.0001.0001.000
DECIS_NO0.0390.0000.0390.0380.2570.0320.0180.0390.0370.1721.0000.0001.0000.1710.1710.172
STLE_CD0.1740.0000.4240.1740.0000.0280.0410.1740.1740.5040.0001.0001.0000.5040.5040.504
ACCT_DVCD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ACCT_DEPT_CD0.0520.1540.9850.0500.1750.3650.3830.0520.0501.0000.1710.5041.0001.0001.0001.000
UPDT_BRCD0.0520.1540.9850.0500.1750.3650.3840.0520.0511.0000.1710.5041.0001.0001.0001.000
REG_BRCD0.0460.1550.9850.0500.1750.3650.3830.0460.0451.0000.1720.5041.0001.0001.0001.000

Missing values

2023-12-13T02:37:29.898364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:37:30.239004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T02:37:30.452414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DEPT_CDDECIS_DYDECIS_NOSTLE_CDCNFM_ENORMT_BNK_CDACCT_DVCDACCT_DEPT_CDACCT_DYSLIP_NOUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCDGENRT_DYCARD_STTL_DY
0THA2020102611117332THA202010261222020-10-26 9:436069THA2020-10-26 9:436069THA2020102620201026
1TNA2020102611119542TNA2020102612020-10-26 8:436077TNA2020-10-26 8:436077TNA2020102620201026
2TAC202010221<NA>125881<NA><NA><NA><NA><NA><NA><NA>2020-10-22 15:186059TAC2020102220201026
3TBB20201022111205322TBB20201026162020-10-26 9:186153TBB2020-10-22 13:206153TBB2020102220201026
4TAD202010211<NA>137781<NA><NA><NA><NA><NA><NA><NA>2020-10-21 17:186040TAD2020102120201026
5TQA202010211<NA>126288<NA><NA><NA><NA><NA><NA><NA>2020-10-21 9:236070TQA2020102120201026
6TPB202010201<NA>13673<NA><NA><NA><NA><NA><NA><NA>2020-10-20 16:186152TPB2020102020201026
7TPA202010191<NA>12283<NA><NA><NA><NA><NA><NA><NA>2020-10-19 16:396066TPA2020101920201026
8THB202010191<NA>114188<NA><NA><NA><NA><NA><NA><NA>2020-10-19 15:136003THB2020101920201026
9TLA2020101611121242TLA2020102612020-10-26 9:016202TLA2020-10-16 10:296202TLA2020101620201026
DEPT_CDDECIS_DYDECIS_NOSTLE_CDCNFM_ENORMT_BNK_CDACCT_DVCDACCT_DEPT_CDACCT_DYSLIP_NOUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCDGENRT_DYCARD_STTL_DY
990TAC20170317111185812TAC2017032432017-03-24 8:476047TAC2017-03-17 18:516047TAC2017031720170324
991THB20170317111037882THB2017032412017-03-24 9:166018THB2017-03-17 11:386018THB2017031720170324
992ACS2017031511<NA>42ACS201703242212017-03-24 9:031214ACS2017-03-15 15:391214ACS2017031520170324
993TAB2017031711112742TAB2017032412017-03-24 8:591764TAB2017-03-17 11:096040TAB2017031720170324
994TPA2017022411118732TPA201702241102017-02-24 15:266066TPA2017-02-24 15:266066TPA2017022420170224
995TOA2017022411113232TOA20170224712017-02-24 14:046076TOA2017-02-24 12:546076TOA2017022420170224
996TAC20170224111185812TAC20170224342017-02-24 10:096047TAC2017-02-24 10:086047TAC2017022420170224
997TMA2017022411109542TMA20170224332017-02-24 10:016036TMA2017-02-24 10:016036TMA2017022420170224
998TJA20170224121142112TJA2017022412017-02-24 9:146053TJA2017-02-24 9:146053TJA2017022420170224
999TLB2017022311115732TLB20170223602017-02-23 14:506054TLB2017-02-23 14:396054TLB2017022320170223